Exploratory Data Analysis in Modern Geophysics: A Practical Tutorial Using R

Exploratory Data Analysis (EDA) has recently enabled the correction of several long standing misconceptions in geophysics, where a number of erroneous theories had been regarded as fundamental laws. These include revisions to the Gutenberg–Richter law, re evaluation of the Omori formula, improved visualisation of plate boundaries around Japan, and renewed prospects for earthquake prediction. To apply EDA effectively to one’s own data, a basic grounding in statistics and the ability to use a statistical software environment are essential. In this article, we introduce the use of R, an open source statistical computing platform, and demonstrate that conducting such analyses is both accessible and straightforward. Earthquake data were obtained from catalogues published by the Japan Meteorological Agency, and all computations were performed in R. The analyses show that modern statistical methods—particularly EDA combined with computational tools—substantially enhance the accuracy, interpretability, and visualisation of seismic data. Key earthquake related quantities are found to follow distinct statistical behaviours, including normal distributions of magnitudes, log normal distributions of released energy, and first order decay patterns with clear half lives in aftershock sequences. Enhanced three dimensional visualisation further clarifies the structural relationships among plate boundaries and seismic activity. Overall, the findings demonstrate that data driven and statistically rigorous approaches not only deepen our understanding of earthquake processes but also challenge several long standing empirical assumptions. This highlights the need for their re evaluation and provides a stronger foundation for future research, including potential advances in earthquake prediction.